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Trend Prediction Service

Idea

The Trend Prediction Service predicts future values for time series using linear and nonlinear regression models. It is a forecasting framework, that has many useful applications in the area of Process & Condition Monitoring.

Typical use cases for the Trend Prediction Service are:

  • Predictive maintenance: Detect if a component's lifetime may be reached in the short-term future.
  • Monitoring of processes: Predict the duration of a process to prevent undesired states (e.g. waiting).
  • Seasonality and trend removal as preparation for other data analytics tasks.

Access

For accessing this service you need to have the respective roles listed in Analytics Services roles and scopes.

Basics

The Prediction Service is a data-driven approach that can be applied to univariate (single input variable) or multivariate (multiple input variables) time-series data. The predicted output is univariate and written into a single target variable.

The service provides the functionality required for estimating the relationships between the variables of a given time series in order to make predictions based on the trained model. These predictions can be used for reasoning about the process represented by the time series.

The trained models are based on a linear or polynomial regression.

Features

The Trend Prediction Service exposes its API for realizing the following tasks:

  • Train (fit) regression models using multivariate time-series data
  • Predict future values
  • Perform training and prediction using one request
  • Read and delete a regression models

Example Scenario

An engineer monitoring the production line of a brewery wants to predict the expected energy consumption (y) for the next month. They assume a linear dependency between the energy consumption and time as well as the load, which are treated as independent variables.

The engineer collects time series data with the energy consumption and load of the production line, feeds the Trend Prediction Service API with this time series and evaluates the predicted future consumption.

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